An Architectural Blueprint for Gated, Meta-Cognitive Forecasting Systems: Integrating Hybrid Models, External Data, and LLM-based Reasoning

The development of predictive forecasting systems has entered a new era. While traditional statistical methods effectively model linear trends and machine learning (ML) excels at capturing non-linearities, neither is sufficient in isolation to handle the complex, dynamic, and event-driven nature of real-world systems. Modern forecasting problems are characterized by regime changes, external shocks, and the influence of unstructured, qualitative information streams such as news and policy.

A robust forecasting system must, therefore, be a multi-faceted, hybrid system. This report provides an architectural blueprint for such a system, predicated on a "separation of concerns" philosophy. It details a system that: 1. Establishes a powerful Baseline Hybrid Engine by decomposing the time series into its linear and non-linear components. 2. Implements a rigorous Hindcasting Validation Framework to ensure model fidelity and provide unbiased performance estimates before deployment. 3. Leverages a Dual-Stream Data Fusion Subsystem that treats structured data (e.g., weather) as "meta-knowledge" to adapt the baseline, while using Large Language Models (LLMs) to quantify unstructured, event-driven data (e.g., news). 4. Deploys an LLM-based Gating Agent as a "meta-controller" that monitors the baseline, reasons over external events, and actively intervenes to handle edge cases and market shocks.

This architecture moves beyond a static model to create a dynamic, explainable, and resilient predictive system.

Architecting the Hybrid Baseline Forecasting Engine: A Decompositional Approach

The foundation of any robust system is a baseline model that accurately captures the primary signal. Time series data is rarely purely linear or non-linear; it is almost always a composite.1 The most successful hybrid architectures are built on the principle of decomposition: using statistical models for what they excel at (linear patterns) and ML models for what they excel at (complex, non-linear relationships).3

1.1 Architectural Pattern 1: Serial Residual Modeling (The Booster Hybrid)

This is the most common and arguably most effective hybrid pattern. It is a serial process where models are "boosted" by subsequent models that correct their errors.5 The process flow is as follows: 1. Fit Statistical Model: A statistical model, such as ARIMA (Autoregressive Integrated Moving Average), SARIMAX, or Prophet, is fit to the primary time series $Y_t$.1 This model captures the transparent linear trends, autocorrelation, and seasonalities.6 This yields a primary forecast, `$\hat{Y}_{\text{STAT}}$`. 2. Extract Residuals: The model's in-sample errors, or residuals, are extracted: `$R_t = Y_t - \hat{Y}_{\text{STAT}}$.4 These residuals represent the non-linear, complex patterns that the statistical model failed to capture.1 3. Fit ML Model: A powerful, non-linear ML model—such as XGBoost, LightGBM, or a Long Short-Term Memory (LSTM) network—is trained to forecast the residuals.2 The target variable for this model is $R_t$. This yields a residual forecast, `$\hat{R}_{\text{ML}}$`. 4. Final Forecast: The final prediction is the linear sum of the two forecasts: `$\hat{Y}_{\text{FINAL}} = \hat{Y}_{\text{STAT}} + \hat{R}_{\text{ML}}$.4

This architecture is well-documented. Classic implementations use ARIMA for the linear component and an Artificial Neural Network (ANN) or LSTM to model the non-linear residuals.1 A particularly potent modern combination is Prophet + XGBoost. While a simple implementation has XGBoost predict the Prophet residuals 10, a more advanced "feature augmentation" method uses Prophet's own decomposed components (e.g., trend, weekly_seasonality, yearly_seasonality) as direct input features for the XGBoost model.14

1.2 Architectural Pattern 2: Parallel Stacked Ensembles

This architecture runs multiple models in parallel and uses a "meta-model" to learn the optimal, weighted combination of their forecasts. It is a competitive, "wisdom-of-the-crowds" approach.15 1. Independent Training: A suite of diverse models (e.g., ARIMA, Prophet, LSTM, LightGBM) are trained independently on the same historical data.6 2. Generate Out-of-Sample Forecasts: Each model generates forecasts for a common validation period. 3. Train Meta-Model (Stacking): A meta-model, which can be a simple Linear Regression or a more complex XGBoost model, is trained. Its features are the forecasts from the base models, and its target is the actual, true value. This meta-model learns the optimal weights for each base model, potentially dynamically.12

One study on transformer oil temperature, for example, used ARIMA, LSTM, and XGBoost in parallel, then fed their predictions as inputs into a final linear regression model. This stacking approach proved more accurate than a simple weighted average or any single model, as the meta-model could learn to dynamically trust the model best suited for the current conditions.12

1.3 Architectural Pattern 3: Hybridizing Time-Series Foundation Models (TSFMs)

This represents the new state-of-the-art for baseline models. TSFMs, such as Amazon's Chronos, Google's Time-LLM, and Nixtla's TimeGPT, are large models pre-trained on massive, diverse collections of time-series datasets.18

These models leverage Transformer architectures (Chronos is T5-based) to perform remarkable zero-shot forecasting—predicting a new time series without any specific training on it.18

Despite their power, TSFMs can suffer from high variance or domain-specific bias when applied to a new, unseen dataset.26 They are significantly improved by applying the same hybrid principles from the patterns above.

• Residual Modeling on TSFMs: A highly effective strategy is to use a simple model (e.g., a regressor for known covariates like price or promotions) to predict the "easy" part of the signal. The TSFM (e.g., Chronos-Bolt) is then fine-tuned to forecast the residuals.28 This simplifies the task for the TSFM, allowing it to focus its power on the most complex part of the signal.
• Ensembling TSFMs: Applying ensemble techniques like bootstrap aggregation (bagging) or regression-based stacking on top of TSFM forecasts can markedly reduce variance, correct systematic bias, and improve overall accuracy.26

The optimal baseline architecture is therefore a meta-hybrid: 1. Layer 1 (TSFM): Use a TSFM (e.g., Chronos) to generate a powerful zero-shot or fine-tuned baseline forecast (`$\hat{Y}_{\text{TSFM}}$`). 2. Layer 2 (Residual Booster): Train an XGBoost model only on the TSFM's historical residuals (`$R_t = Y_t - \hat{Y}_{\text{TSFM}}$) and domain-specific covariates. 3. Final Forecast = `$\hat{Y}_{\text{TSFM}} + \hat{R}_{\text{XGBoost}}$. This combines the vast general-purpose knowledge of the TSFM with a specialized, fast-learning booster that corrects its domain-specific errors.

Table 1: Comparison of Hybrid Baseline Architectures

Architecture	Core Principle	Component Models	Strengths	Weaknesses
Serial-Residual 2	Decomposition (Linear + Non-linear)	1. Statistical (ARIMA) 2. ML (XGBoost)	Interpretable baseline; ML focuses only on complex patterns.	Error from Model 1 propagates to Model 2.
Parallel-Stacked 12	Ensembling (Wisdom of Crowds)	1. Base (ARIMA, LSTM) 2. Meta (Regression)	Robust; resilient to single-model failure; captures diverse patterns.	High computational cost; "black box" meta-model.
TSFM-Residual 28	Pre-training + Specialization	1. TSFM (Chronos) 2. ML (XGBoost)	SOTA zero-shot baseline; corrects domain-specific TSFM bias.	TSFM is computationally heavy; potential for overfitting residual model.

A Systematic Framework for Validation and Iterative Refinement

A model's performance on training data is irrelevant. A robust validation strategy, used for iterative improvement, is non-negotiable. Standard k-fold cross-validation is invalid for time-series data as it shuffles data and breaks temporal dependencies.32 The correct approach is Backtesting, a term often used interchangeably with Hindcasting or Time-Series Cross-Validation, which strictly respects the temporal order of observations.32

2.1 Backtesting vs. Hindcasting: A Critical Distinction

While often conflated, these terms serve two distinct purposes:

• Backtesting: This is primarily an evaluation method. It simulates operational deployment on historical data (e.g., a hedge fund testing a trading algorithm) to get a reliable, non-biased estimate of a model's future performance.33
• Hindcasting: This is primarily a scientific and developmental method. It involves "predicting the past" 36 to diagnose model failures and iteratively refine the model's parameters and structure to better replicate observed historical dynamics.37

The request to "validate past data" is backtesting, while using it to "improve the performance" is hindcasting.38

2.2 Walk-Forward Validation Mechanics: The Gold Standard

Walk-forward validation (or "rolling forecast") is the mechanism for implementing both backtesting and hindcasting.33 The system must specify one of two approaches:

• Expanding Window Validation: The process is (Train on $t_1...t_n$, predict $t_{n+1}$), then (Train on $t_1...t_{n+1}$, predict $t_{n+2}$), and so on.32 This is optimal when all historical data is considered relevant and the underlying process is relatively stable, assuming more data is always better.44
• Rolling (Sliding) Window Validation: The process is (Train on $t_k...t_n$, predict $t_{n+1}$), then (Train on $t_{k+1}...t_{n+1}$, predict $t_{n+2}$).42 The training window size is fixed. This is essential for non-stationary data or processes with "concept drift," where old patterns become obsolete and recent data is more predictive.41

Table 2: Hindcasting/Backtesting Validation Strategies

Strategy	Process	Primary Use Case	Key Risk
Simple Train-Test Split	Single split point.	Rapid prototyping, large stationary data.	Fails to evaluate model across different regimes.
Walk-Forward Expanding Window 32	Train: $[1...n]$, Test: $[n+1]$. Train: $[1...n+1]$, Test: $[n+2]$.	Stable, stationary processes where more data is always better.	Computationally heavy; old, irrelevant data can bias the model.
Walk-Forward Rolling Window 32	Train: $[k...n]$, Test: $[n+1]$. Train: $[k+1...n+1]$, Test: $[n+2]$.	Non-stationary data with "concept drift." Recent data is more predictive.	Choice of window size is a critical hyperparameter.

2.3 The Iterative Refinement Loop (The Goal of Hindcasting)

This is the process of using the hindcast results to improve the model. The flow is: 1. Execute Hindcast: Run a full walk-forward validation (e.g., rolling window) over a significant historical period. 2. Analyze Hindcast Error: Collect all out-of-sample prediction errors. Systematically analyze them.45 Identify where the model failed: Does it consistently fail during specific regimes (e.g., high volatility, or, as one study found, during ice-melt periods 45)? 3. Refine Model: Use these insights to adjust the model in an iterative loop.37 For example, if the hindcast shows consistent over-estimation, it points to a systematic bias that can be corrected.45 A crucial finding in one study showed that adding time-varying climate covariates improved the hindcast fit but decreased forecast skill.48 This is a critical warning against overfitting the hindcast; the goal is not a perfect historical fit, but the best generalizable model.

2.4 Critical Challenge: Validation with Exogenous Variables

Backtesting with external data (weather, news) is extremely high-risk for lookahead bias.35 The hindcasting framework must be architected to enforce the "A Priori" Rule: At any simulated time $t_n$, the model only has access to external information that would have been realistically available at that moment.

For example, to hindcast sales for "April 3rd," the model can use a weather forecast for April 3rd made on April 2nd, but it cannot use the actual observed weather for April 3rd.41 This requires meticulous timestamping and data partitioning.

This leads to the necessity of a two-stage validation process. A single validation run cannot be used for both model development and final evaluation. If a model is tuned based on the results of a backtest (hindcasting), that backtest is now "tainted" and can no longer be used as an unbiased "final exam." 1. Stage 1: Hindcast-Refinement Mode: Use a "development" validation set (e.g., 2018-2019 data) to run rolling-window hindcasts. Analyze the errors.45 and iteratively refine the model architecture, features, and parameters.37 2. Stage 2: Backtest-Evaluation Mode: Once the model is "locked," execute a final walk-forward backtest 33 on a completely separate, held-out test set (e.g., 2020-2021 data). The performance on this second set is the only reliable estimate of future operational performance.

Feature Engineering and Fusion of External Data Streams

Integrating external data like weather, news, and policy is where most forecasting systems either fail due to poor fusion or create significant predictive power. The architecture must be deliberate in how it fuses heterogeneous data.

3.1 Standard Feature Engineering for Synchronous Covariates

This is the baseline for integrating structured, time-stamped data like weather 49 or economic indicators.

• Time-Based Features: Day-of-week, month, quarter, is_holiday, is_weekend.52
• Lag Features: Lagged values of the target variable.52
• Rolling Statistics: Moving averages, standard deviations, min/max over a defined window.52

A critical architectural constraint is the distinction between covariates:

• Future-Known Covariates: (e.g., Public holidays, planned promotions). These can be fed directly into the model for future prediction horizons.54
• Past-Only Covariates: (e.g., Observed weather, competitor pricing). These are only known for the past. To use them for future forecasts, the system must also forecast the covariates themselves (e.g., use an actual weather forecast).54

3.2 Fusion Architectures for Asynchronous & Heterogeneous Data

A common problem is data heterogeneity: daily sales, hourly weather, and weekly policy reports.55 These cannot be simply joined in a table.

• Level 1 (Simple Fusion): Interpolation. Resample all data to a common frequency (e.g., daily). Use forward-filling, backward-filling, or linear interpolation.56 This is simple but introduces significant bias and "smears" the impact of event-driven data.58
• Level 2 (Advanced Fusion): Imputation. Use advanced models like Generative Adversarial Networks (GANs) or Bayesian inference to impute missing values with a measure of uncertainty.59
• Level 3 (Architectural Fusion): Multi-Modal Models. Design the neural network to accept different data types.
  • Late Fusion (Decision-Level): Train separate models (one for time-series data, one for text data) and fuse their final predictions.60
  • Intermediate Fusion (Feature-Level): Create embeddings for each data stream (e.g., an LSTM for time-series, a Transformer for text) and concatenate these embedding vectors before feeding them to a final set of prediction layers.60 This is generally the most powerful approach as it allows the model to find cross-modal patterns.

3.3 Advanced Integration: External Data as "Meta-Knowledge"

This is a paradigm shift in data integration, proposed for adaptive load forecasting.62 Instead of external data (weather, calendar) being just another input feature, it functions as meta-knowledge that dynamically adapts the forecasting model itself.62

The Hypernetwork architecture is as follows: 1. A Base Network (e.g., an RNN/LSTM) forecasts the primary signal (e.g., energy load). 2. A Hypernetwork (a small neural net) takes the external data (e.g., temperature, day-of-week) as its input. 3. The output of the Hypernetwork is the set of weights (parameters) for the Base Network.62

The implication is that the forecasting model is not fixed. On a hot day, the hypernetwork (fed "temp=95F") generates weights for the base model that make it behave like a "hot day model." On a weekend, it generates "weekend model" weights. This allows the system to dynamically adapt its internal logic to the external context.62

This leads to a dual-stream integration architecture for external data. Weather, news, and policy are not alike.

• Stream 1 (Adaptive Modulation): For structured, continuous data (weather, economic indicators). This stream should use the Hypernetwork/Meta-Knowledge approach 62 to dynamically modulate the parameters of the baseline hybrid model.
• Stream 2 (Event-Driven): For unstructured, event-based data (news, policy). This stream cannot be modeled as a simple regressor, as it often represents shocks or regime changes.63 It must be processed by the LLM-based subsystems.

Table 3: External Data Integration Framework

Data Type	Example(s)	Key Challenge	SOTA Integration Method	Architectural Role
Structured, Continuous	Weather 49, Economic Indicators	Different frequency, non-linear impact.	Hypernetwork / Meta-Representation 62	Dynamically modulates the baseline model's parameters.
Structured, Event	Public Holidays, Known Promotions	Known in advance, binary impact.	Future-Known Covariate 54	Direct input feature for the baseline model.
Unstructured, Event	News Articles, Policy Reports 63	Asynchronous, text-based, represents "shocks."	LLM Feature Extractor / Reasoning Agent 65	Quantifies text for features OR triggers an intervention.

Leveraging Large Language Models for Edge Case and Event-Driven Dynamics

Using LLMs for "handling edge cases" is the most novel part of this system. This is a new and contentious field of research 67, but a clear consensus is emerging: LLMs are best used not as standalone forecasters, but as "co-pilots" for reasoning, anomaly detection, and unstructured data processing.

The roles for the LLM represent a three-tiered hierarchy of intervention: Monitor, Embed, and Agent.

4.1 Role 1: The LLM as an Anomaly Detector & Explainer (Reactive Monitor)

The LLM can be used to identify and, more importantly, explain anomalies in the time-series.

• Architecture 1: "Prompter" Pipeline71 The time-series data is converted into a string of numbers.72 This string is fed to an LLM with a direct prompt, such as: "The following is a time-series of daily sales. Identify any anomalous data points and provide a likely explanation for each."71 This is simple and leverages the LLM's world knowledge for explanation 74, but may only detect "trivial" anomalies.75
• Architecture 2: "Detector" Pipeline71 This uses the LLM as a zero-shot forecaster.76
1. The time-series history is tokenized 76 and fed to an LLM (e.g., GPT-3, LLaMA).78
2. The LLM performs "next-token prediction" to generate a forecast of the next data point(s).76
3. The residual ($Y\_t \- \\hat{Y}\_{\\text{LLM-Forecast}}$) is calculated.
  4. A large residual indicates the LLM's "expectation" of the pattern was violated, thus flagging an anomaly.71 This is a more robust detection method.

4.2 Role 2: The LLM as a Qualitative-to-Quantitative Feature Extractor (Proactive Embedder)

This is the primary, most reliable method for integrating the "news" and "policy" data streams. The LLM acts as a translation layer, reading unstructured text and producing structured, quantitative features for the baseline hybrid model.65

• Method A: Sentiment & Topic Scoring. Use a domain-specific LLM (like FinBERT) 84 or a general LLM (GPT-4) 86 to read news articles and output a numerical sentiment score (e.g., -1.0 to 1.0).65 This sentiment_score becomes a powerful exogenous regressor.
• Method B: Event Extraction. Use an LLM to read text and extract a structured JSON of key information, such as "datetime," "stakeholders," and "summary" from a project report.89
• Method C: Policy & Event Quantization. This is the most advanced feature-extraction method, using prompt engineering to force the LLM to quantify a qualitative concept.90 For example, a prompt can ask the LLM to act as a senior analyst, read an FOMC policy statement 91, and rate it on a "Hawkish" (tightening) to "Dovish" (easing) scale from -1.0 to +1.0, outputting a JSON with the 'score' and 'rationale'.65 This policy_score feature is an invaluable, forward-looking predictor.

4.3 Role 3: The LLM as a Reasoning Agent for Forecast Intervention (Active Handler)

This is the SOTA architecture for "handling edge cases." The LLM moves from a passive feature provider to an active decision-maker.

The "From News to Forecast" (NeurIPS 2024) framework provides a clear blueprint.66 1. News Filtering: An LLM-based agent iteratively filters a high volume of news to identify articles relevant to the forecast.93 2. Alignment & Reasoning: The agent "aligns news content with time series fluctuations".66 It is prompted to perform "human-like reasoning" to evaluate the baseline forecast in the context of the selected news.93 3. Refinement: The agent analyzes the impact of its own news selection on forecast accuracy, refining its selection logic iteratively.93

In this role, the LLM-Agent receives the baseline model's forecast and the critical, filtered news. It reasons: "The baseline model predicts a 5% increase, following the seasonal trend. However, I have identified 3 critical news articles reporting an unexpected factory strike.63 This event breaks the historical pattern. The baseline forecast is therefore highly likely to be incorrect.".95 At this point, the system can flag the forecast for human review or override the baseline forecast entirely.

Table 4: Architectural Roles for LLMs in the Forecasting Pipeline

Role	Primary Function	Data Input	Data Output	Architectural Stage
LLM-Monitor 71	Anomaly Detection & Explanation	Time-Series String	Anomaly Flag + Natural Language Rationale 74	Post-processing / Monitoring
LLM-Embed 65	Qualitative-to-Quantitative Feature Extraction	News Articles, Policy Docs	Quantitative Features (e.g., Sentiment Score) 90	Pre-processing (Feature Engineering)
Intervention** 66	Reasoning & Intervention	`Baseline Forecast + Filtered News`	Forecast Override + Chain-of-Thought Rationale 96	Real-time Gating / Intervention

Blueprint for an End-to-End Integrated Forecasting System

This final section synthesizes all previous components into a single, cohesive system architecture. The central challenge is orchestrating the Baseline Hybrid (Sec I), the External Data (Sec III), and the LLM Agents (Sec IV).

5.1 The Gated, Meta-Controller Architecture

The most robust architecture is a **Gated Mixture of Experts (MoE).97 An MoE consists of (1) multiple "expert" sub-models and (2) a "gating network" that dynamically chooses which expert's output to use for a given input.97

In this proposed system architecture:

• Expert 1: The Baseline Hybrid Engine (e.g., Chronos + XGBoost-Residual). This model is trained on all historical data and the quantified (LLM-Embed) features. This is the "normal" expert.
• Expert 2: The LLM Zero-Shot Forecaster (e.g., GPT-4 or Chronos zero-shot). This is the "novel event" expert.
• Expert 3: A Simple Statistical Model (e.g., AutoARIMA). This is the "stable/mean-reversion" expert.
• The Gating Mechanism: The LLM-Agent (Role 3) from Section IV.66 This agent acts as the meta-controller for the entire system.99

The End-to-End Prediction Flow at time $t$ is as follows: 1. Ingestion: Structured time-series data and unstructured text (news, policy) are ingested. 2. Quantization: The LLM-Embed (Role 2) parses all text and generates quantitative features (e.g., policy_score, news_sentiment).65 3. Parallel Forecasting: The structured data + quantified features are sent to Expert 1 (Baseline) and Expert 3 (Statistical). Expert 2 (LLM Zero-Shot) receives the tokenized history. All three generate a candidate forecast. 4. Meta-Cognitive Gating: The LLM-Agent (Gater) receives: (a) Candidate Forecast 1, (b) Candidate Forecast 2, (c) Candidate Forecast 3, and (d) the raw text of high-impact news/policy.66 5. Reasoning & Selection: The LLM-Agent reasons over this input.68

• If news is "normal": It selects Expert 1's (Baseline) forecast.
• If news is "highly anomalous" (e.g., "COVID-19 pandemic declared," "major war begins"): It reasons that all historical models (Experts 1 & 3)are invalid. It may select Expert2(Zero-Shot) or flag for human intervention. 6. **Explainable Output:** The final system output is (1) the selected forecast and (2) the LLM-Agent's Chain-of-Thought (CoT) rationale for its decision.96`

5.2 Recommended Open-Source Stacks & Tooling

• Hybrid Modeling & Validation: Darts 54 and Sktime 104 are excellent Python libraries with built-in support for multiple models (ARIMA, Prophet, DL), exogenous regressors, and walk-forward validation.41 PyCaret's time-series module is also strong.105
• TSFMs (Foundation Models): Chronos models are available on Hugging Face and integrated into AutoGluon-TS.20
• LLM Anomaly Detection: SigLLM is an open-source library specifically designed for the "Prompter" and "Detector" pipelines.71
• LLM NLP & Gating: Hugging Face transformers 84 for feature extraction (e.g., FinBERT) and libraries like LangChain or LlamaIndex for building the reasoning agent.100

5.3 Implementation Risks and Mitigation Strategies

• Challenge 1: Computational Cost & Latency. LLM inference is orders of magnitude slower and more expensive than traditional models.106
  • Mitigation: The Gated MoE architecture. The expensive LLM-Agent 66 is not run for every forecast. It is an "exception handler" triggered only when the LLM-Embed (Role 2) detects high-impact news or the LLM-Monitor (Role 1) detects a high-anomaly score. The vast majority of forecasts are handled by the efficient Baseline Hybrid.
• Challenge 2: Overfitting to External Data. Using thousands of news articles as features creates a high-dimensional, noisy dataset where spurious correlations are guaranteed.110
  • Mitigation: The rigorous two-stage Hindcasting/Backtesting framework (Sec II) is the only solution.33 Feature selection and dimensionality reduction (PCA) on text embeddings are also critical.111
• Challenge 3: LLM Reliability & Hallucination. The LLM-Agent is not infallible. It can misinterpret news, fail at numerical reasoning 67, or hallucinate.113
  • Mitigation: The system must be designed for human-in-the-loop (HITL) validation. The LLM-Agent's "override" should be treated as a recommendation for a human analyst.
• Challenge 4: Explainability & Trust (The "Black Box" Problem). The full system is a complex problem).115
  • Mitigation (System-Level Solution): This architecture is more explainable than a single end-to-end deep learning model.
    1. The Baseline Hybrid is inherently interpretable (e.g., Prophet's components are clear).4
2. The LLM-Agent, the least interpretable part, is mandated to provide a Chain-of-Thought (CoT) rationale 96 for every intervention it makes.103 This means that for the most critical "edge case" forecasts, the system provides a full, natural-language explanation of why it is overriding the baseline, citing the specific news or events that drove its decision.

Conclusion and Recommendations

The architecture detailed in this report moves beyond a single forecasting model to propose a "meta-cognitive" system. This system is designed to be resilient by separating "normal" forecasting from "edge case" handling.

The key architectural pillars are: 1. A Decomposed Baseline: A TSFM (e.g., Chronos) provides a powerful, generalist forecast, while a specialized ML model (e.g., XGBoost) learns to correct its domain-specific residual errors. 2. Rigorous, Two-Stage Validation: A "Hindcast-Refinement" stage is used for iterative model development, while a separate, "Backtest-Evaluation" stage on held-out data provides an unbiased estimate of true operational performance. 3. Dual-Stream Data Fusion: Structured data (weather) is used as "meta-knowledge" via hypernetworks to adapt the baseline model's parameters, while unstructured data (news, policy) is quantified by an "LLM-Embed" pipeline to become machine-readable features. 4. LLM-based Gating: A "Gated Mixture of Experts" architecture is controlled by an "LLM-Agent." This agent acts as the system's meta-controller, reasoning over real-time news to select the most appropriate forecast from its "experts" (e.g., the baseline, or a zero-shot model) and providing a full, auditable rationale for its decision.

The future of forecasting is not a single, larger model. It is the development of these multi-agent, gated systems that can reason about their own predictions in the context of a dynamic and event-driven world.

Works cited

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